from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-01-01 14:11:10.132278
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 01, Jan, 2021
Time: 14:11:13
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.4111
Nobs: 158.000 HQIC: -45.4471
Log likelihood: 1718.56 FPE: 9.02689e-21
AIC: -46.1556 Det(Omega_mle): 5.19599e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459171 0.158458 2.898 0.004
L1.Burgenland 0.138035 0.080691 1.711 0.087
L1.Kärnten -0.234736 0.064932 -3.615 0.000
L1.Niederösterreich 0.112326 0.188241 0.597 0.551
L1.Oberösterreich 0.256412 0.160937 1.593 0.111
L1.Salzburg 0.171675 0.083390 2.059 0.040
L1.Steiermark 0.081384 0.115566 0.704 0.481
L1.Tirol 0.149149 0.077277 1.930 0.054
L1.Vorarlberg 0.006821 0.073792 0.092 0.926
L1.Wien -0.123682 0.155574 -0.795 0.427
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.514420 0.205013 2.509 0.012
L1.Burgenland 0.011627 0.104397 0.111 0.911
L1.Kärnten 0.367035 0.084008 4.369 0.000
L1.Niederösterreich 0.133287 0.243545 0.547 0.584
L1.Oberösterreich -0.187021 0.208220 -0.898 0.369
L1.Salzburg 0.187817 0.107890 1.741 0.082
L1.Steiermark 0.251632 0.149519 1.683 0.092
L1.Tirol 0.142577 0.099981 1.426 0.154
L1.Vorarlberg 0.176857 0.095471 1.852 0.064
L1.Wien -0.582540 0.201282 -2.894 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.293924 0.068860 4.268 0.000
L1.Burgenland 0.106963 0.035065 3.050 0.002
L1.Kärnten -0.025097 0.028217 -0.889 0.374
L1.Niederösterreich 0.072609 0.081803 0.888 0.375
L1.Oberösterreich 0.291632 0.069938 4.170 0.000
L1.Salzburg -0.005089 0.036238 -0.140 0.888
L1.Steiermark -0.020479 0.050221 -0.408 0.683
L1.Tirol 0.088344 0.033582 2.631 0.009
L1.Vorarlberg 0.125792 0.032067 3.923 0.000
L1.Wien 0.077707 0.067607 1.149 0.250
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204047 0.079942 2.552 0.011
L1.Burgenland -0.012568 0.040708 -0.309 0.758
L1.Kärnten 0.022106 0.032758 0.675 0.500
L1.Niederösterreich 0.025422 0.094967 0.268 0.789
L1.Oberösterreich 0.412146 0.081192 5.076 0.000
L1.Salzburg 0.097417 0.042070 2.316 0.021
L1.Steiermark 0.182135 0.058302 3.124 0.002
L1.Tirol 0.033205 0.038986 0.852 0.394
L1.Vorarlberg 0.096327 0.037228 2.588 0.010
L1.Wien -0.061598 0.078487 -0.785 0.433
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.593441 0.166629 3.561 0.000
L1.Burgenland 0.071559 0.084851 0.843 0.399
L1.Kärnten 0.001038 0.068280 0.015 0.988
L1.Niederösterreich -0.048749 0.197947 -0.246 0.805
L1.Oberösterreich 0.155256 0.169236 0.917 0.359
L1.Salzburg 0.056915 0.087690 0.649 0.516
L1.Steiermark 0.113639 0.121525 0.935 0.350
L1.Tirol 0.211008 0.081262 2.597 0.009
L1.Vorarlberg 0.004768 0.077596 0.061 0.951
L1.Wien -0.140780 0.163596 -0.861 0.389
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157197 0.116097 1.354 0.176
L1.Burgenland -0.027552 0.059119 -0.466 0.641
L1.Kärnten -0.012278 0.047573 -0.258 0.796
L1.Niederösterreich 0.174310 0.137917 1.264 0.206
L1.Oberösterreich 0.397514 0.117913 3.371 0.001
L1.Salzburg -0.029809 0.061097 -0.488 0.626
L1.Steiermark -0.046219 0.084671 -0.546 0.585
L1.Tirol 0.189997 0.056618 3.356 0.001
L1.Vorarlberg 0.041966 0.054064 0.776 0.438
L1.Wien 0.162685 0.113984 1.427 0.154
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.238196 0.145398 1.638 0.101
L1.Burgenland 0.064897 0.074040 0.877 0.381
L1.Kärnten -0.046329 0.059580 -0.778 0.437
L1.Niederösterreich -0.030657 0.172726 -0.177 0.859
L1.Oberösterreich -0.103392 0.147673 -0.700 0.484
L1.Salzburg 0.008206 0.076517 0.107 0.915
L1.Steiermark 0.381749 0.106041 3.600 0.000
L1.Tirol 0.519127 0.070908 7.321 0.000
L1.Vorarlberg 0.195469 0.067710 2.887 0.004
L1.Wien -0.223890 0.142752 -1.568 0.117
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.121980 0.170663 0.715 0.475
L1.Burgenland 0.014374 0.086905 0.165 0.869
L1.Kärnten -0.114794 0.069933 -1.641 0.101
L1.Niederösterreich 0.218105 0.202739 1.076 0.282
L1.Oberösterreich 0.009317 0.173333 0.054 0.957
L1.Salzburg 0.222518 0.089813 2.478 0.013
L1.Steiermark 0.142462 0.124467 1.145 0.252
L1.Tirol 0.094040 0.083229 1.130 0.259
L1.Vorarlberg 0.014752 0.079475 0.186 0.853
L1.Wien 0.288491 0.167557 1.722 0.085
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.586564 0.093709 6.259 0.000
L1.Burgenland -0.020074 0.047719 -0.421 0.674
L1.Kärnten 0.000152 0.038399 0.004 0.997
L1.Niederösterreich -0.009583 0.111322 -0.086 0.931
L1.Oberösterreich 0.279345 0.095175 2.935 0.003
L1.Salzburg 0.011242 0.049315 0.228 0.820
L1.Steiermark 0.000611 0.068343 0.009 0.993
L1.Tirol 0.077449 0.045700 1.695 0.090
L1.Vorarlberg 0.170381 0.043639 3.904 0.000
L1.Wien -0.091102 0.092003 -0.990 0.322
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.139574 -0.005049 0.206560 0.244578 0.060231 0.098754 -0.081971 0.162460
Kärnten 0.139574 1.000000 -0.005506 0.186153 0.130676 -0.145229 0.173581 0.031632 0.297323
Niederösterreich -0.005049 -0.005506 1.000000 0.263997 0.082245 0.200661 0.103092 0.043820 0.353315
Oberösterreich 0.206560 0.186153 0.263997 1.000000 0.275771 0.291999 0.108454 0.075316 0.106819
Salzburg 0.244578 0.130676 0.082245 0.275771 1.000000 0.143819 0.071766 0.080246 -0.022598
Steiermark 0.060231 -0.145229 0.200661 0.291999 0.143819 1.000000 0.101736 0.086127 -0.132607
Tirol 0.098754 0.173581 0.103092 0.108454 0.071766 0.101736 1.000000 0.149323 0.135758
Vorarlberg -0.081971 0.031632 0.043820 0.075316 0.080246 0.086127 0.149323 1.000000 0.099921
Wien 0.162460 0.297323 0.353315 0.106819 -0.022598 -0.132607 0.135758 0.099921 1.000000